Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis

Objectives To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis. Methods Hand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort. Results In total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%±1% for erosions, 91%±2% for osteitis and 85%±2% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90±2%), osteitis (78±8%) and synovitis (69±7%), which remained consistent in the validation dataset. Conclusions We developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.

Table S18 -Macro AUC and balanced accuracy for all pathologies and all MRI sequences and combination of sequences during cross-validation using the Swin transformer.
Bold text highlights the best results for the prediction of each lesion score, underlined text highlights the best performing individual sequence for each lesion.Results that are both bold and underlined reflect that one sequence alone achieves the best overall performance to predict the lesion score without need of combination with other sequences.In one fold of Syonvitis T1 occurred training instabilities due to the network architecture.Hence, we stopped training after 50 epochs in this case.

Table S2 -
Patient clinical and imaging characteristics of the validation dataset.

Table S3 -
Detailed evaluation for erosions of each MRI sequence and combination of sequences during cross-validation.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S4 -
Detailed evaluation for osteitis of each MRI sequence and combination of sequences during cross-validation.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) RMD Open doi: 10.1136/rmdopen-2024-004273

Table S5 -
Detailed evaluation for synovitis of each MRI sequence and combination of sequences during cross-validation.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S6 -
Detailed evaluation for erosions of each MRI sequence and combination of sequences during cross-validation when the same number of available data is used for each sequence (seq equal).Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S7 -
Detailed evaluation for osteitis of each MRI sequence and combination of sequences during cross-validation when the same number of available data is used for each sequence (seq equal).Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S8 -
Detailed evaluation for synovitis of each MRI sequence and combination of sequences during cross-validation when the same number of available data is used for each sequence (seq equal).Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S9 -
Detailed evaluation for erosions of each MRI sequence and combination of sequences during independent validation.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S10 -
Detailed evaluation for osteitis of each MRI sequence and combination of sequences during independent validation.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S11 -
Detailed evaluation for synovitis of each MRI sequence and combination of sequences during independent validation.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S12 -
Detailed evaluation for erosion of each MRI sequence and combination of sequences during independent validation of the PARAJA cohort.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S13 -
Detailed evaluation for osteitis of each MRI sequence and combination of sequences during independent validation of the PARAJA cohort.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S14 -
Detailed evaluation for synovitis of each MRI sequence and combination of sequences during independent validation of the PARAJA cohort.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S15 -
Detailed evaluation for erosion of each MRI sequence and combination of sequences during independent validation of the PSARTROS cohort.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S16 -
Detailed evaluation for osteitis of each MRI sequence and combination of sequences during independent validation of the PSARTROS cohort.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.

Table S17 -
Detailed evaluation for synovitis of each MRI sequence and combination of sequences during independent validation of the PSARTROS cohort.Depicted are weighted AUC, balanced accuracy, macro AUC, weighed and macro PR-AUC.